With the development of the communication technology, frequency spectrum resources become increasingly scarce. In order to solve this problem, simultaneous transmission of multiple signal flows in a same frequency band may be realized through a large-scale multi-input multi-output (MIMO) system. The system is divided into an uplink and a downlink. In the uplink, encoded signals from antennas of a plurality of users are sent to a plurality of antennas of a base station. Since there are interference and channel noise between various antennas, the base station needs to decode the received encoded signals, so as to realize signal detection.
Currently, the common signal detection method includes linear algorithms like a zero-forcing (ZF) detecting method and a minimum mean square error (MMSE) etc., and non-linear algorithms such as a sphere decoding (SD) and a K-Best algorithm etc. Non-linear algorithms for detecting the accuracy are widely applied in the large-scale multi-input multi-output system. However, for the large-scale multi-input multi-output system, if an antenna size grows in hundreds, realizing non-linearity on limited hardware resources of the base station will lead to a high computation complexity and a low degree of parallelism.